On the Application of the Novel Thin Spray-on Liner (TSL): A Progress Report in Mining Operations

Stability is the main problem in underground excavations that can result in rock-related accidents or closure of mines. This is often controlled by the geological condition of the rockmass and the in situ stress field where excavation is being carried out. Over the years, conventional support systems such as bolts, shotcrete and mesh have been the standard support systems used in difficult ground conditions to maintain stability of underground openings. The novel Thin Spray Liner (TSL) is a technology with an improvement in terms of support capabilities for difficult geological conditions. This technology is predicted to replace conventional support systems, as it offers a significant yielding capacity that is better than that of the traditional techniques of controlling strata. The performance of the TSL lies between that of shotcrete and wire mesh. The TSL has numerous advantages regarding rock reinforcement such as rapid curing period, fast application, high tensile strength, high adhesion properties that prevent movement of rock keyblocks, improved cycle times, reduction in material-handling compared to shotcrete, and a significant ability to penetrate joints. These advantages increase the allowance for mechanisation and improvement of safety in excavations. Despite various benefits of TSLs, there are shortcomings which make end-users to be sceptical about the technology. This study provides a detailed review of the mechanism, properties, composition and types, as well as the general and mining applications of TSLs. It also describes the challenges faced by the mining industry and other end-users in accepting the technology as a solution for stability issues in underground openings. In addition, some insightful suggestions are made as recommendations for future work on the development of suitable, effective and efficient thin surface rock support technology within limitations/constraints.

     

Volatility modeling of rainfall time series

Networks of rain gauges can provide a better insight into the spatial and temporal variability of rainfall, but they tend to be too widely spaced for accurate estimates. A way to estimate the spatial variability of rainfall between gauge points is to interpolate between them. This paper evaluates the spatial autocorrelation of rainfall data in some locations in Peninsular Malaysia using geostatistical technique. The results give an insight on the spatial variability of rainfall in the area, as such, two rain gauges were selected for an in-depth study of the temporal dependence of the rainfall data-generating process. It could be shown that rainfall data are affected by nonlinear characteristics of the variance often referred to as variance clustering or volatility, where large changes tend to follow large changes and small changes tend to follow small changes. The autocorrelation structure of the residuals and the squared residuals derived from autoregressive integrated moving average (ARIMA) models were inspected, the residuals are uncorrelated but the squared residuals show autocorrelation, and the Ljung–Box test confirmed the results. A test based on the Lagrange multiplier principle was applied to the squared residuals from the ARIMA models. The results of this auxiliary test show a clear evidence to reject the null hypothesis of no autoregressive conditional heteroskedasticity (ARCH) effect. Hence, it indicates that generalized ARCH (GARCH) modeling is necessary. An ARIMA error model is proposed to capture the mean behavior and a GARCH model for modeling heteroskedasticity (variance behavior) of the residuals from the ARIMA model. Therefore, the composite ARIMA–GARCH model captures the dynamics of daily rainfall in the study area. On the other hand, seasonal ARIMA model became a suitable model for the monthly average rainfall series of the same locations treated.     

Vegetation Chronosequence in Hong Kong: Soil Properties and SuccessionalDevelopment

The soil properties of a vegetation chronosequence in Hong Kong involving a grassland, a 22-year mixed woodland, a 52-year forest and two graduation 150-year old climax ( feng shui ) forests were examined. The objectives were to test the three hypotheses: (1) there are no significant differences in soil chemical properties between different climax forests, (2) exotic species are inferior to native species in soil amelioration; and (3) organic carbon, nitrogen and cation nutrients tend to accumulate in the soils during successional development of the vegetation. The results show that the soils along the vegetation chronosequence are strongly acidic in reaction, contained moderate to high levels of organic carbon, high exchangeable acidity, and low levels of mineral nitrogen (NH⊂4⊂> and NO⊂3⊂), available phosphorus and cation nutrients. All the three hypotheses are rejected. While the feng shui forests differed markedly in soil properties, exotic species were superior to native species in augmenting soil organic carbon, mineral nitrogen and exchangeable Ca. There was no accumulation of organic carbon, nitrogen and cation nutrients with ecological succession, due to the influence of patchy fire, species composition, parent materials and distance from the sea.     

Fuzzy MCDM-based GIS model for subsea oil pipeline route optimization: An integrated approach

Proper pipeline route selection is an integral component of a typical oil exploration and transportation project. Improperly selected routes could have severe consequences including pipe failures, oil spillage, and environmental disasters. Consequently, technologies like geographic information systems (GIS) are increasingly being used to facilitate the oil pipeline route selection procedure—especially for onshore routing projects. Surprisingly, not much has been documented on the application of GIS to offshore pipeline routing. With recent discoveries on the merits of offshore oil exploration, it is necessary to extend the analytical capabilities of GIS to the unique offshore domain. However, concerns have been raised regarding the limitations of GIS in accurately prioritizing diverse selection criteria in typical multi-criteria decision-making (MCDM) problems like route selection. Consequently, this paper addresses the offshore/subsea pipeline routing constraint using a hybrid decision support system (DSS), which integrates a GIS and fuzzy logic-based approximate reasoning (AR) models for optimal performance. The resultant spatial decision support system (SDSS) was successfully applied to a case study in Malaysia. The AR algorithm calculated the significance level of the multiple criteria using various fuzzy linguistic variables and membership functions. The aggregated priority ranking from different pipeline routing experts showed that the overall influence of the environmental criteria (61.4%) significantly exceeded that of other equally important criteria in the study area. These rankings were inputted into the SDSS to simulate various probable routes. Final results accurately highlighted an optimal route, which places a premium on the protection of environmental features in the subsea study area—in alignment with the preferences of majority of the experts.     

GIS-based analytic hierarchy process as a multicriteria decision analysis instrument: a review

Information and communication technology, which has been incorporated and provided in the Geographic Information System (GIS), is valuable and effective geospatial information for the decision makers in improving their decisions in planning and development. The integration of this GIS using the multicriteria decision analysis approach provides an environment to the decision makers in citing areas using land suitability analysis procedures. This review paper particularly examines the GIS-based analytic hierarchy process as a multicriteria analysis/evaluation technique in land suitability analysis by means of literature reviews and surveys.     

Geospatial analysis of Maize yield vulnerability to climate change in Nigeria

The fifth assessment report (AR5) predicted that land temperatures would rise faster over Africa than other global averages while changes in rainfall are uncertain across Sub-Saharan Africa. These portend water availability challenges with direct impacts on agricultural production. Existing studies on yield vulnerability in Nigeria are mostly at a national scale, which is not adequate for local decision making. This study provides a spatially explicit model of Maize yield vulnerabilities across the growing areas (GA). Thereby, turning available data into actionable information to support development actions. Yield vulnerability index was constructed as a relationship among exposure, yield sensitivity and adaptive capacity. Exposure was computed as the ratio between long and short-term climatic factors. Yield sensitivities were expressed as the ratio between expected and actual yield. Adaptive capacity was captured using a combination of socio-economic proxies. The result shows that Maize yields were vulnerable to climate variability across most of the GAs. Exposure values indicate a very high level of climate variability with the northern region more exposed. Yield sensitivity ranges between ranges 0.47 and 0.95, and highest along the northern extremes, moderate sensitivities were observed across large tracts of the north-west, northeast, south-east and south–south geopolitical regions. Adaptive capacity is highly variable ranging between 0.27 and 1. Yield vulnerability ranges between 0.46 and 1.51. The general assumption of a north–south divide for yield vulnerability was invalidated. Vulnerability is more disparate beyond latitudinal differences. The model presented, creates a framework to support targeted response, and opportunity for building resilience to climate change impact for crop yield.

     

A stochastic model of low flows

A model is developed for annual low flow hydrographs. Its two primary components reflect the fact that hydrologic processes during streamflow rise (function of water input) and recession (function of basin storage) are different. Durations of periods of rise (wet intervals) and recession (dry intervals) are modelled by discrete probability distributions — negative binomial for dry intervals and negative binomial or modified logarithmic series for wet intervals depending on goodness of fit. During wet intervals, the total inflow is modelled by the lognormal distribution and daily amounts are allocated according to a pattern-averaged model. During dry intervals, the flow recedes according to a deterministic-stochastic recession model. The model was applied to three Canadian basins with drainage area ranging from 2210 to 22000 km² to generate 50 realizations of low flow hydrographs. The resulting two standard-error confidence band for the simulated probability distribution of annual minimum 7-day flows enclosed the probability distribution estimated from the observed record. A sensitivity analysis for the three basins revealed that in addition to the recession submodel, the most important submodel is that describing seasonality. The state of the basin at the beginning of the low flow period is of marginal importance and the daily distribution of input is unimportant.     

Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image

Coastline identification is important for surveying and mapping reasons. Coastline serves as the basic point of reference and is used on nautical charts for navigation purposes. Its delineation has become crucial and more important in the wake of the many recent earthquakes and tsunamis resulting in complete change and redraw of some shorelines. In a tropical country like Malaysia, presence of cloud cover hinders the application of optical remote sensing data. In this study a semi-automated technique and procedures are presented for shoreline delineation from RADARSAT-1 image. A scene of RADARSAT-1 satellite image was processed using enhanced filtering technique to identify and extract the shoreline coast of Kuala Terengganu, Malaysia. RADSARSAT image has many advantages over the optical data because of its ability to penetrate cloud cover and its night sensing capabilities. At first, speckles were removed from the image by using Lee sigma filter which was used to reduce random noise and to enhance the image and discriminate the boundary between land and water. The results showed an accurate and improved extraction and delineation of the entire coastline of Kuala Terrenganu. The study demonstrated the reliability of the image averaging filter in reducing random noise over the sea surface especially near the shoreline. It enhanced land-water boundary differentiation, enabling better delineation of the shoreline. Overall, the developed techniques showed the potential of radar imagery for accurate shoreline mapping and will be useful for monitoring shoreline changes during high and low tides as well as shoreline erosion in a tropical country like Malaysia.     

Deficiencies of the vertical slice method in assessment of the seismic earth pressures

Acta Geotechnica - Conventionally, when the method of slice is adopted to assess the earth pressures behind the retaining wall considering a curvilinear failure surface, the mobilized soil mass...     

A spatial decision support system (SDSS) for sustainable tourism planning in Johor Ramsar sites, Malaysia

The need for a sustainable approach in tourism development is very often addressed among the academia, the authorities and the stakeholders, as well as the apparent need for tools, which will guide the decision environment in evaluation and planning. This research aims to identify conservation and compatible areas for tourism development in Johor Ramsar sites, using spatial modeling in geographic information system (GIS). The study describes a methodological approach based on the integrated use of GIS and multi criteria decision evaluation (MCE) to identify nature conservation and development priorities among the wetland areas. A set of criteria was defined to evaluate wetland biodiversity conservation and development. Having defined the criteria, the next step was selecting suitable indicators and variables to measure the selected criteria. Subsequently the criteria were evaluated from conservation and tourism development point of view. These criteria were then weighted using the pair wise comparison technique of MCE and the results were integrated into GIS. Several conservation scenarios were generated so as to simulate different evaluation perspectives. The scenarios were then compared to highlight the most feasible and to propose a conservation and development strategy for the wetland areas. The generation and comparison of conservation and development scenarios highlighted the critical issues of the decision problem. This study represents an important contribution to effective decision-making because it allows one to gradually narrow down a problem.